Instructions

The following tutorial will let you reproduce the plots that we are going to create at the workshop using R.
Please read carefully and follow the steps. Wherever you see the Code icon on the right you can click on it to see the actual code used in that section.

Introduction

This tutorial will focus on analysing the updated data of the worldwide Novel Corona virus (COVID-19) pandemic.
There are several data sources available online. We will use the data collected from a range of official sources and hosted on the Our World in Data website (Mathieu et al. 2021).

Analyse Data in R

To run R and RStudio on Binder, click on this badge - Launch Rstudio Binder.

Start RStudio and create a new project named Workshop3 in a new folder (if you need a reminder ho to do it, check out Workshop1 Tutorial on BB).
Once RStudio restarts inside the project’s folder, create a new R script named Workshop3.R and 2 new folders, one named data for our input data and another named output for our plots.

Install Extra Packages

For this analysis we will again use some packages from the Tidyverse, but this time we load the specific packages (which are supposed to be pre-installed on your computers) to try and avoid having to download the entire tidyverse. In addition to the Tidyverse packages we’ve got to know in the previous workshop we will use the plotly package to create interactive plots, paletteer for custom color palettes, readxl to read MS-Excel file, scales to format large numbers, lubridate to better handle dates, glue to paste together strings, patchwork to include several plots in a single figure and a few others to assist in getting the data into shape.
To install these packages, we will introduce a package called pacman that will assist in loading the required packages and installing them if they’re not already installed. To install it we use the install.packages('pacman') command, please note that the package name need to be quoted and that we only need to perform it once, or when we want or need to update the package. Once the package was installed, we can load its functions using the library(pacman) command and then load/install all the other packages at once with p_load() function.

# install required packages - needed only once! (comment with a # after first use)
install.packages('pacman')
# load required packages
library(pacman)
p_load(dplyr, tidyr, ggplot2, readr,  paletteer, glue, scales, plotly, lubridate, patchwork, visdat)

More information on installing and using R packages can be found in this tutorial.

Read Data

Now that we’ve got RStudio up and running and our packages installed and loaded, we can read data into R from our local computer or from web locations using dedicated functions specific to the file type (.csv, .txt, .xlsx, etc.).

We will use the read_csv() command/function from the readr package (part of the tidyverse) to load the data directly from a file on the Our World in Data website into a variable of type data frame (table). If we don’t want to use external packages, we can use the read.csv() function from base R, which won’t automatically parse columns containing dates and in previous versions of R (< 4.0) will slightly change the structure of the resulting data frame (all text columns will be converted into factors).
> Note that in this case, we need to specify the column types because the data contains a lot of missing values that interfere with the automatic parsing of the column types._

# read data from Our World in Data website
covid_data <- read_csv("https://covid.ourworldindata.org/data/owid-covid-data.csv", 
                       col_types = paste(c("c", "f", "c", "D", rep("d", 29), 
                                           "c", rep("d", 33)), collapse = ""))

Data Exploration

Let’s use built-in functions for a brief data exploration, such as head() to show the first 10 rows of the data and str() for the type of data in each column (see detailed information on each variable in the data repository on GitHub):

#explore the data frame
head(covid_data) # show first 10 rows of the data and typr of variables
## # A tibble: 6 x 67
##   iso_code continent location  date       total_cases new_cases new_cases_smoot~
##   <chr>    <fct>     <chr>     <date>           <dbl>     <dbl>            <dbl>
## 1 AFG      Asia      Afghanis~ 2020-02-24           5         5               NA
## 2 AFG      Asia      Afghanis~ 2020-02-25           5         0               NA
## 3 AFG      Asia      Afghanis~ 2020-02-26           5         0               NA
## 4 AFG      Asia      Afghanis~ 2020-02-27           5         0               NA
## 5 AFG      Asia      Afghanis~ 2020-02-28           5         0               NA
## 6 AFG      Asia      Afghanis~ 2020-02-29           5         0               NA
## # ... with 60 more variables: total_deaths <dbl>, new_deaths <dbl>,
## #   new_deaths_smoothed <dbl>, total_cases_per_million <dbl>,
## #   new_cases_per_million <dbl>, new_cases_smoothed_per_million <dbl>,
## #   total_deaths_per_million <dbl>, new_deaths_per_million <dbl>,
## #   new_deaths_smoothed_per_million <dbl>, reproduction_rate <dbl>,
## #   icu_patients <dbl>, icu_patients_per_million <dbl>, hosp_patients <dbl>,
## #   hosp_patients_per_million <dbl>, weekly_icu_admissions <dbl>, ...
str(covid_data) # show data structure
## spec_tbl_df [167,936 x 67] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ iso_code                                  : chr [1:167936] "AFG" "AFG" "AFG" "AFG" ...
##  $ continent                                 : Factor w/ 6 levels "Asia","Europe",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ location                                  : chr [1:167936] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ date                                      : Date[1:167936], format: "2020-02-24" "2020-02-25" ...
##  $ total_cases                               : num [1:167936] 5 5 5 5 5 5 5 5 5 5 ...
##  $ new_cases                                 : num [1:167936] 5 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_smoothed                        : num [1:167936] NA NA NA NA NA NA 0.714 0 0 0 ...
##  $ total_deaths                              : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths                                : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths_smoothed                       : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_cases_per_million                   : num [1:167936] 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 ...
##  $ new_cases_per_million                     : num [1:167936] 0.126 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_smoothed_per_million            : num [1:167936] NA NA NA NA NA NA 0.018 0 0 0 ...
##  $ total_deaths_per_million                  : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths_per_million                    : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths_smoothed_per_million           : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ reproduction_rate                         : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ icu_patients                              : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ icu_patients_per_million                  : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ hosp_patients                             : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ hosp_patients_per_million                 : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_icu_admissions                     : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_icu_admissions_per_million         : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_hosp_admissions                    : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_hosp_admissions_per_million        : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests                               : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests                                 : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests_per_thousand                  : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_per_thousand                    : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed                        : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed_per_thousand           : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ positive_rate                             : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_per_case                            : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_units                               : chr [1:167936] NA NA NA NA ...
##  $ total_vaccinations                        : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ people_vaccinated                         : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ people_fully_vaccinated                   : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_boosters                            : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations                          : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations_smoothed                 : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_vaccinations_per_hundred            : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ people_vaccinated_per_hundred             : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ people_fully_vaccinated_per_hundred       : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ total_boosters_per_hundred                : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations_smoothed_per_million     : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_people_vaccinated_smoothed            : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ new_people_vaccinated_smoothed_per_hundred: num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ stringency_index                          : num [1:167936] 8.33 8.33 8.33 8.33 8.33 ...
##  $ population                                : num [1:167936] 39835428 39835428 39835428 39835428 39835428 ...
##  $ population_density                        : num [1:167936] 54.4 54.4 54.4 54.4 54.4 ...
##  $ median_age                                : num [1:167936] 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 ...
##  $ aged_65_older                             : num [1:167936] 2.58 2.58 2.58 2.58 2.58 ...
##  $ aged_70_older                             : num [1:167936] 1.34 1.34 1.34 1.34 1.34 ...
##  $ gdp_per_capita                            : num [1:167936] 1804 1804 1804 1804 1804 ...
##  $ extreme_poverty                           : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ cardiovasc_death_rate                     : num [1:167936] 597 597 597 597 597 ...
##  $ diabetes_prevalence                       : num [1:167936] 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 ...
##  $ female_smokers                            : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ male_smokers                              : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ handwashing_facilities                    : num [1:167936] 37.7 37.7 37.7 37.7 37.7 ...
##  $ hospital_beds_per_thousand                : num [1:167936] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
##  $ life_expectancy                           : num [1:167936] 64.8 64.8 64.8 64.8 64.8 ...
##  $ human_development_index                   : num [1:167936] 0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 ...
##  $ excess_mortality_cumulative_absolute      : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality_cumulative               : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality                          : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality_cumulative_per_million   : num [1:167936] NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   iso_code = col_character(),
##   ..   continent = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
##   ..   location = col_character(),
##   ..   date = col_date(format = ""),
##   ..   total_cases = col_double(),
##   ..   new_cases = col_double(),
##   ..   new_cases_smoothed = col_double(),
##   ..   total_deaths = col_double(),
##   ..   new_deaths = col_double(),
##   ..   new_deaths_smoothed = col_double(),
##   ..   total_cases_per_million = col_double(),
##   ..   new_cases_per_million = col_double(),
##   ..   new_cases_smoothed_per_million = col_double(),
##   ..   total_deaths_per_million = col_double(),
##   ..   new_deaths_per_million = col_double(),
##   ..   new_deaths_smoothed_per_million = col_double(),
##   ..   reproduction_rate = col_double(),
##   ..   icu_patients = col_double(),
##   ..   icu_patients_per_million = col_double(),
##   ..   hosp_patients = col_double(),
##   ..   hosp_patients_per_million = col_double(),
##   ..   weekly_icu_admissions = col_double(),
##   ..   weekly_icu_admissions_per_million = col_double(),
##   ..   weekly_hosp_admissions = col_double(),
##   ..   weekly_hosp_admissions_per_million = col_double(),
##   ..   total_tests = col_double(),
##   ..   new_tests = col_double(),
##   ..   total_tests_per_thousand = col_double(),
##   ..   new_tests_per_thousand = col_double(),
##   ..   new_tests_smoothed = col_double(),
##   ..   new_tests_smoothed_per_thousand = col_double(),
##   ..   positive_rate = col_double(),
##   ..   tests_per_case = col_double(),
##   ..   tests_units = col_character(),
##   ..   total_vaccinations = col_double(),
##   ..   people_vaccinated = col_double(),
##   ..   people_fully_vaccinated = col_double(),
##   ..   total_boosters = col_double(),
##   ..   new_vaccinations = col_double(),
##   ..   new_vaccinations_smoothed = col_double(),
##   ..   total_vaccinations_per_hundred = col_double(),
##   ..   people_vaccinated_per_hundred = col_double(),
##   ..   people_fully_vaccinated_per_hundred = col_double(),
##   ..   total_boosters_per_hundred = col_double(),
##   ..   new_vaccinations_smoothed_per_million = col_double(),
##   ..   new_people_vaccinated_smoothed = col_double(),
##   ..   new_people_vaccinated_smoothed_per_hundred = col_double(),
##   ..   stringency_index = col_double(),
##   ..   population = col_double(),
##   ..   population_density = col_double(),
##   ..   median_age = col_double(),
##   ..   aged_65_older = col_double(),
##   ..   aged_70_older = col_double(),
##   ..   gdp_per_capita = col_double(),
##   ..   extreme_poverty = col_double(),
##   ..   cardiovasc_death_rate = col_double(),
##   ..   diabetes_prevalence = col_double(),
##   ..   female_smokers = col_double(),
##   ..   male_smokers = col_double(),
##   ..   handwashing_facilities = col_double(),
##   ..   hospital_beds_per_thousand = col_double(),
##   ..   life_expectancy = col_double(),
##   ..   human_development_index = col_double(),
##   ..   excess_mortality_cumulative_absolute = col_double(),
##   ..   excess_mortality_cumulative = col_double(),
##   ..   excess_mortality = col_double(),
##   ..   excess_mortality_cumulative_per_million = col_double()
##   .. )

Descriptive Statistics

We can also produce some descriptive statistics to better understand the data and the nature of each variable. The summary() function (as can be guessed by its name) provides a quick summary of basic descriptive statistics, such as the mean, min, max and quantiles for continuous numerical values.

# summary of variables in my data
summary(covid_data)
##    iso_code                 continent       location        
##  Length:167936      Asia         :36536   Length:167936     
##  Class :character   Europe       :37445   Class :character  
##  Mode  :character   Africa       :39802   Mode  :character  
##                     North America:25169                     
##                     South America: 9608                     
##                     Oceania      : 9329                     
##                     NA's         :10047                     
##       date             total_cases          new_cases       new_cases_smoothed
##  Min.   :2020-01-01   Min.   :        1   Min.   :      0   Min.   :      0   
##  1st Qu.:2020-09-10   1st Qu.:     2050   1st Qu.:      1   1st Qu.:      7   
##  Median :2021-03-18   Median :    26658   Median :     79   Median :    108   
##  Mean   :2021-03-12   Mean   :  2593114   Mean   :  11766   Mean   :  11752   
##  3rd Qu.:2021-09-13   3rd Qu.:   304329   3rd Qu.:   1071   3rd Qu.:   1159   
##  Max.   :2022-03-12   Max.   :456790241   Max.   :4115804   Max.   :3445357   
##                       NA's   :3047        NA's   :3223      NA's   :5212      
##   total_deaths       new_deaths      new_deaths_smoothed
##  Min.   :      1   Min.   :    0.0   Min.   :    0.000  
##  1st Qu.:     80   1st Qu.:    0.0   1st Qu.:    0.143  
##  Median :    791   Median :    2.0   Median :    2.429  
##  Mean   :  58259   Mean   :  170.7   Mean   :  172.317  
##  3rd Qu.:   7369   3rd Qu.:   20.0   3rd Qu.:   21.286  
##  Max.   :6040665   Max.   :18021.0   Max.   :14689.286  
##  NA's   :20951     NA's   :20930     NA's   :23039      
##  total_cases_per_million new_cases_per_million new_cases_smoothed_per_million
##  Min.   :     0.0        Min.   :    0.00      Min.   :    0.000             
##  1st Qu.:   634.4        1st Qu.:    0.04      1st Qu.:    1.632             
##  Median :  4845.7        Median :   11.49      Median :   18.982             
##  Mean   : 30349.8        Mean   :  170.25      Mean   :  169.311             
##  3rd Qu.: 38911.8        3rd Qu.:  101.97      3rd Qu.:  122.224             
##  Max.   :706541.9        Max.   :51427.49      Max.   :16052.608             
##  NA's   :3812            NA's   :3988          NA's   :5971                  
##  total_deaths_per_million new_deaths_per_million
##  Min.   :   0.0           Min.   :  0.000       
##  1st Qu.:  18.9           1st Qu.:  0.000       
##  Median : 131.4           Median :  0.126       
##  Mean   : 515.9           Mean   :  1.684       
##  3rd Qu.: 725.5           3rd Qu.:  1.369       
##  Max.   :6339.5           Max.   :453.772       
##  NA's   :21703            NA's   :21682         
##  new_deaths_smoothed_per_million reproduction_rate  icu_patients    
##  Min.   :  0.000                 Min.   :-0.07     Min.   :    0.0  
##  1st Qu.:  0.017                 1st Qu.: 0.80     1st Qu.:   28.0  
##  Median :  0.292                 Median : 0.99     Median :  146.0  
##  Mean   :  1.686                 Mean   : 1.00     Mean   :  899.6  
##  3rd Qu.:  1.773                 3rd Qu.: 1.18     3rd Qu.:  594.0  
##  Max.   :144.167                 Max.   : 6.12     Max.   :28891.0  
##  NA's   :23785                   NA's   :40773     NA's   :144227   
##  icu_patients_per_million hosp_patients    hosp_patients_per_million
##  Min.   :  0.00           Min.   :     0   Min.   :   0.00          
##  1st Qu.:  3.96           1st Qu.:   130   1st Qu.:  26.68          
##  Median : 13.39           Median :   709   Median :  85.67          
##  Mean   : 23.91           Mean   :  4188   Mean   : 167.76          
##  3rd Qu.: 34.72           3rd Qu.:  2769   3rd Qu.: 229.25          
##  Max.   :177.28           Max.   :154540   Max.   :1544.08          
##  NA's   :144227           NA's   :143064   NA's   :143064           
##  weekly_icu_admissions weekly_icu_admissions_per_million weekly_hosp_admissions
##  Min.   :   0.0        Min.   :  0.00                    Min.   :     0        
##  1st Qu.:  48.0        1st Qu.:  3.94                    1st Qu.:   325        
##  Median : 220.0        Median : 11.06                    Median :  1350        
##  Mean   : 468.5        Mean   : 15.41                    Mean   :  6009        
##  3rd Qu.: 665.0        3rd Qu.: 20.34                    3rd Qu.:  5204        
##  Max.   :4838.0        Max.   :221.21                    Max.   :154696        
##  NA's   :162419        NA's   :162419                    NA's   :156892        
##  weekly_hosp_admissions_per_million  total_tests          new_tests      
##  Min.   :  0.00                     Min.   :        0   Min.   :      1  
##  1st Qu.: 23.98                     1st Qu.:   381040   1st Qu.:   2481  
##  Median : 73.64                     Median :  1970205   Median :   9804  
##  Mean   :104.08                     Mean   : 17575921   Mean   :  68302  
##  3rd Qu.:142.60                     3rd Qu.:  9258870   3rd Qu.:  38937  
##  Max.   :781.38                     Max.   :827499466   Max.   :3740296  
##  NA's   :156892                     NA's   :97813       NA's   :99900    
##  total_tests_per_thousand new_tests_per_thousand new_tests_smoothed
##  Min.   :    0.00         Min.   :  0.00         Min.   :      0   
##  1st Qu.:   35.48         1st Qu.:  0.26         1st Qu.:   2103   
##  Median :  183.24         Median :  0.94         Median :   8546   
##  Mean   :  754.25         Mean   :  3.23         Mean   :  60698   
##  3rd Qu.:  719.71         3rd Qu.:  2.90         3rd Qu.:  35835   
##  Max.   :29597.68         Max.   :534.01         Max.   :3080396   
##  NA's   :97813            NA's   :99900          NA's   :82474     
##  new_tests_smoothed_per_thousand positive_rate   tests_per_case    
##  Min.   :  0.00                  Min.   :0.00    Min.   :     1.0  
##  1st Qu.:  0.23                  1st Qu.:0.02    1st Qu.:     7.1  
##  Median :  0.90                  Median :0.06    Median :    17.3  
##  Mean   :  2.91                  Mean   :0.10    Mean   :   197.7  
##  3rd Qu.:  2.70                  3rd Qu.:0.14    3rd Qu.:    52.6  
##  Max.   :147.60                  Max.   :0.99    Max.   :422065.6  
##  NA's   :82474                   NA's   :87867   NA's   :88464     
##  tests_units        total_vaccinations  people_vaccinated  
##  Length:167936      Min.   :0.000e+00   Min.   :0.000e+00  
##  Class :character   1st Qu.:6.079e+05   1st Qu.:3.942e+05  
##  Mode  :character   Median :4.860e+06   Median :2.976e+06  
##                     Mean   :1.768e+08   Mean   :9.024e+07  
##                     3rd Qu.:3.042e+07   3rd Qu.:1.773e+07  
##                     Max.   :1.097e+10   Max.   :5.000e+09  
##                     NA's   :122301      NA's   :124535     
##  people_fully_vaccinated total_boosters      new_vaccinations  
##  Min.   :1.000e+00       Min.   :1.000e+00   Min.   :       0  
##  1st Qu.:2.790e+05       1st Qu.:2.725e+03   1st Qu.:    6248  
##  Median :2.313e+06       Median :4.814e+05   Median :   41567  
##  Mean   :7.152e+07       Mean   :2.057e+07   Mean   : 1174415  
##  3rd Qu.:1.393e+07       3rd Qu.:4.256e+06   3rd Qu.:  278607  
##  Max.   :4.460e+09       Max.   :1.455e+09   Max.   :54855684  
##  NA's   :127260          NA's   :149912      NA's   :130218    
##  new_vaccinations_smoothed total_vaccinations_per_hundred
##  Min.   :       0          Min.   :  0.00                
##  1st Qu.:    1048          1st Qu.: 12.44                
##  Median :    9282          Median : 59.63                
##  Mean   :  519990          Mean   : 73.90                
##  3rd Qu.:   66068          3rd Qu.:124.97                
##  Max.   :43535350          Max.   :338.97                
##  NA's   :82018             NA's   :122301                
##  people_vaccinated_per_hundred people_fully_vaccinated_per_hundred
##  Min.   :  0.00                Min.   :  0.00                     
##  1st Qu.:  8.80                1st Qu.:  5.26                     
##  Median : 36.77                Median : 27.68                     
##  Mean   : 38.19                Mean   : 32.92                     
##  3rd Qu.: 64.80                3rd Qu.: 58.63                     
##  Max.   :124.69                Max.   :121.59                     
##  NA's   :124535                NA's   :127260                     
##  total_boosters_per_hundred new_vaccinations_smoothed_per_million
##  Min.   : 0.00              Min.   :     0                       
##  1st Qu.: 0.01              1st Qu.:   677                       
##  Median : 3.39              Median :  2166                       
##  Mean   :12.88              Mean   :  3289                       
##  3rd Qu.:21.58              3rd Qu.:  4713                       
##  Max.   :92.69              Max.   :117497                       
##  NA's   :149912             NA's   :82018                        
##  new_people_vaccinated_smoothed new_people_vaccinated_smoothed_per_hundred
##  Min.   :       0               Min.   : 0.00                             
##  1st Qu.:     422               1st Qu.: 0.02                             
##  Median :    3944               Median : 0.07                             
##  Mean   :  213614               Mean   : 0.15                             
##  3rd Qu.:   26838               3rd Qu.: 0.19                             
##  Max.   :21396353               Max.   :11.75                             
##  NA's   :83360                  NA's   :83360                             
##  stringency_index   population        population_density    median_age   
##  Min.   :  0.00   Min.   :4.700e+01   Min.   :    0.137   Min.   :15.10  
##  1st Qu.: 40.74   1st Qu.:1.172e+06   1st Qu.:   36.253   1st Qu.:22.20  
##  Median : 54.63   Median :8.478e+06   Median :   85.129   Median :29.90  
##  Mean   : 54.48   Mean   :1.473e+08   Mean   :  464.163   Mean   :30.57  
##  3rd Qu.: 70.37   3rd Qu.:3.393e+07   3rd Qu.:  212.865   3rd Qu.:39.10  
##  Max.   :100.00   Max.   :7.875e+09   Max.   :20546.766   Max.   :48.20  
##  NA's   :36712    NA's   :1082        NA's   :18580       NA's   :28775  
##  aged_65_older    aged_70_older    gdp_per_capita     extreme_poverty
##  Min.   : 1.144   Min.   : 0.526   Min.   :   661.2   Min.   : 0.10  
##  1st Qu.: 3.507   1st Qu.: 2.063   1st Qu.:  4449.9   1st Qu.: 0.60  
##  Median : 6.614   Median : 3.915   Median : 12951.8   Median : 2.20  
##  Mean   : 8.761   Mean   : 5.534   Mean   : 19639.4   Mean   :13.58  
##  3rd Qu.:14.178   3rd Qu.: 8.678   3rd Qu.: 27936.9   3rd Qu.:21.20  
##  Max.   :27.049   Max.   :18.493   Max.   :116935.6   Max.   :77.60  
##  NA's   :30283    NA's   :29521    NA's   :28095      NA's   :75846  
##  cardiovasc_death_rate diabetes_prevalence female_smokers   male_smokers  
##  Min.   : 79.37        Min.   : 0.990      Min.   : 0.10   Min.   : 7.70  
##  1st Qu.:168.71        1st Qu.: 5.310      1st Qu.: 1.90   1st Qu.:21.60  
##  Median :243.81        Median : 7.170      Median : 6.30   Median :31.40  
##  Mean   :260.24        Mean   : 8.212      Mean   :10.63   Mean   :32.78  
##  3rd Qu.:329.94        3rd Qu.:10.430      3rd Qu.:19.30   3rd Qu.:41.30  
##  Max.   :724.42        Max.   :30.530      Max.   :44.00   Max.   :78.10  
##  NA's   :29835         NA's   :22594       NA's   :60864   NA's   :62333  
##  handwashing_facilities hospital_beds_per_thousand life_expectancy
##  Min.   :  1.19         Min.   : 0.10              Min.   :53.28  
##  1st Qu.: 19.35         1st Qu.: 1.30              1st Qu.:69.50  
##  Median : 49.84         Median : 2.40              Median :75.05  
##  Mean   : 50.78         Mean   : 3.03              Mean   :73.58  
##  3rd Qu.: 83.24         3rd Qu.: 4.00              3rd Qu.:78.93  
##  Max.   :100.00         Max.   :13.80              Max.   :86.75  
##  NA's   :98702          NA's   :43082              NA's   :11163  
##  human_development_index excess_mortality_cumulative_absolute
##  Min.   :0.394           Min.   : -37726.1                   
##  1st Qu.:0.602           1st Qu.:    -67.3                   
##  Median :0.743           Median :   3482.2                   
##  Mean   :0.726           Mean   :  38223.0                   
##  3rd Qu.:0.845           3rd Qu.:  25682.2                   
##  Max.   :0.957           Max.   :1111864.5                   
##  NA's   :30360           NA's   :162189                      
##  excess_mortality_cumulative excess_mortality
##  Min.   :-28.45              Min.   :-95.92  
##  1st Qu.: -0.68              1st Qu.: -0.74  
##  Median :  6.13              Median :  7.23  
##  Mean   :  9.48              Mean   : 15.99  
##  3rd Qu.: 14.60              3rd Qu.: 23.02  
##  Max.   :111.01              Max.   :375.00  
##  NA's   :162189              NA's   :162189  
##  excess_mortality_cumulative_per_million
##  Min.   :-1826.60                       
##  1st Qu.:  -28.58                       
##  Median :  478.56                       
##  Mean   :  985.44                       
##  3rd Qu.: 1667.35                       
##  Max.   : 9153.06                       
##  NA's   :162189
# find extreme rows
covid_data %>% arrange(desc(total_cases))
## # A tibble: 167,936 x 67
##    iso_code continent location date       total_cases new_cases new_cases_smoot~
##    <chr>    <fct>     <chr>    <date>           <dbl>     <dbl>            <dbl>
##  1 OWID_WRL <NA>      World    2022-03-12   456790241   1556688         1651453 
##  2 OWID_WRL <NA>      World    2022-03-11   455233553   1807277         1623829.
##  3 OWID_WRL <NA>      World    2022-03-10   453426276   1817503         1608428.
##  4 OWID_WRL <NA>      World    2022-03-09   451609116   1881823         1617683.
##  5 OWID_WRL <NA>      World    2022-03-08   449727293   1844922         1585029.
##  6 OWID_WRL <NA>      World    2022-03-07   447882371   1498717         1541542.
##  7 OWID_WRL <NA>      World    2022-03-06   446383654   1153241         1528276.
##  8 OWID_WRL <NA>      World    2022-03-05   445230413   1363321         1515639.
##  9 OWID_WRL <NA>      World    2022-03-04   443867092   1699470         1510840 
## 10 OWID_WRL <NA>      World    2022-03-03   442167635   1882290         1495768.
## # ... with 167,926 more rows, and 60 more variables: total_deaths <dbl>,
## #   new_deaths <dbl>, new_deaths_smoothed <dbl>, total_cases_per_million <dbl>,
## #   new_cases_per_million <dbl>, new_cases_smoothed_per_million <dbl>,
## #   total_deaths_per_million <dbl>, new_deaths_per_million <dbl>,
## #   new_deaths_smoothed_per_million <dbl>, reproduction_rate <dbl>,
## #   icu_patients <dbl>, icu_patients_per_million <dbl>, hosp_patients <dbl>,
## #   hosp_patients_per_million <dbl>, weekly_icu_admissions <dbl>, ...

What are the metadata columns that describe our observations?

continent 
location  
date

Why do we have observations with the continent as NA?

# check which location have continent as NA
covid_data %>% filter(is.na(continent)) %>% count(location)
## # A tibble: 13 x 2
##    location                n
##    <chr>               <int>
##  1 Africa                759
##  2 Asia                  781
##  3 Europe                780
##  4 European Union        780
##  5 High income           781
##  6 International         765
##  7 Low income            749
##  8 Lower middle income   781
##  9 North America         781
## 10 Oceania               778
## 11 South America         750
## 12 Upper middle income   781
## 13 World                 781
Some rows contain summarised data of entire continents/World, we'll need to remove those

We can see that most of our data contains ‘0’ (check the difference between the median and the mean in total_cases and total_deaths columns). Just to confirm that, let’s plot a histogram of all the confirmed cases

ggplot(covid_data, aes(x=total_cases)) +
  geom_histogram(fill="lightskyblue") +
  theme_bw(def_text_size)

The data is evolving over days (a time-series), to there’s no point treating it as a random population sample.

Time-series plot

Let’s look at confirmed cases and total deaths data for the 10 most affected countries (to date). To find out these countries so we need to wrangle our data a little bit using the following steps:

  1. First we remove all observations for combined continents with filter(!is.na(continent)
  2. Then we group it by location with group_by()
  3. Then we sort it within each location by date (from latest to earliest) with arrange(desc(date))
  4. We select just the most recent data point from each location with slice(1) and remove grouping with ungroup()
  5. Next we arrange it by descending order of total deaths and select the top 10 observations (one for each location)
  6. Finally, we subset our original data to contain just the countries from our vector with inner_join()

Optional step:

  1. We can recode the location variable as a factor and order it so the countries will be ordered in the legend by the number of cases with

Then we can look at the data as a table and make a plot with the number of cases in the y-axis and date in the x-axis.

# find the 10 most affected countries (to date)
latest_data <- covid_data %>% filter(!is.na(continent)) %>% 
  group_by(location) %>% arrange(desc(date)) %>% slice(1) %>% ungroup() 
most_affected_countries <- latest_data  %>%  
  arrange(desc(total_deaths)) %>% slice(1:10) %>% 
  select(location)

# subset just the data from the 10 most affected countries and order them from the most affected to the least one
most_affected_data <- covid_data %>% 
  inner_join(most_affected_countries) %>% 
  mutate(Country=factor(location, levels = most_affected_countries$location))

# create a line plot the data of total cases
ggplot(most_affected_data, aes(x=date, y=total_cases, colour=Country)) +
  geom_line(size=0.75) + scale_y_continuous(labels=comma) + 
  scale_color_paletteer_d("rcartocolor::Bold") +
  labs(color="Country", y = "Total COVID-19 cases") +
  theme_bw(def_text_size)

It’s a bit hard to figure out how the pandemic evolved because the numbers in US, Brazil and India are an order of magnitude larger than the rest (which are very close to each other). How can we make it more visible (and also improve how of the dates appear in the x-axis)?

# better formatting of date axis, log scale 
plot <- ggplot(most_affected_data, aes(x=date, y=total_cases, colour=Country)) +
  geom_line(size=0.75) + scale_y_log10(labels=comma) + 
  scale_x_date(NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  scale_color_paletteer_d("rcartocolor::Bold") +
  labs(color="Country", y = "Total COVID-19 cases") +
  theme_bw(def_text_size)
# show an interactive plot
ggplotly(plot)

Why did we get a warning message and why the graphs don’t start at the bottom of the x-axis? How can we solve it? What can we infer from the graph (exponential increase)?

What happens when we take the log of 0?? Can we remove those 0s with the `filter()` function (or add a very small number to them)?
We can see a very similar trend for most countries and while the curve has flattened substantially in April last year, the numbers are still rising. It is also evident that Europe got hit by a second wave arount October last year and India in April this year.

Total deaths

Let’s have a look at the total deaths in these countries (and get rid of the minor grid lines to make Frank happy)

# create a line plot the data of total deaths
ggplot(most_affected_data, aes(x=date, y=total_deaths, colour=Country)) +
  geom_line(size=0.75) + scale_y_continuous(labels=comma) + 
  scale_x_date(NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  scale_color_paletteer_d("rcartocolor::Bold") +
  labs(color="Country", y = "Total deaths") +
  theme_bw(def_text_size) +  
  theme(panel.grid.minor = element_blank()) # remove minor grid lines

Vaccination rates

Let’s have a look at the number of vaccinated people.

# vaccination rates
ggplot(most_affected_data, aes(x=date, y=people_vaccinated, colour=Country)) +
  geom_line(size=0.75) + scale_y_continuous(labels=comma) + 
  scale_color_paletteer_d("rcartocolor::Bold") +
  scale_x_date(NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  labs(color="Country", y="People Vaccinated") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank())

The graphs are “broken”, meaning that it is not continuous and we have some missing data.
Let’s visualise some of the variables in our data and assess “missingness”.

# visualise missingness
vis_dat(covid_data %>% filter(date>dmy("01-01-2021")) %>% 
          select(continent, location, total_cases, total_deaths, 
                 hosp_patients, people_vaccinated, people_fully_vaccinated))

# find which countries has the most number of observations (least missing data)
covid_data %>% filter(!is.na(continent), !is.na(people_vaccinated)) %>% # group_by(location) %>% 
  count(location) %>% arrange(desc(n)) %>% print(n=30)
## # A tibble: 219 x 2
##    location           n
##    <chr>          <int>
##  1 Norway           464
##  2 United States    454
##  3 Latvia           453
##  4 Denmark          451
##  5 Canada           449
##  6 Israel           449
##  7 Liechtenstein    445
##  8 Switzerland      445
##  9 Czechia          441
## 10 Italy            441
## 11 Lithuania        441
## 12 Chile            440
## 13 Estonia          440
## 14 Germany          440
## 15 Slovenia         440
## 16 France           439
## 17 Belgium          437
## 18 Ireland          436
## 19 Singapore        433
## 20 United Kingdom   425
## 21 Greece           421
## 22 India            411
## 23 Bahrain          409
## 24 Brazil           409
## 25 Malta            407
## 26 Indonesia        406
## 27 Ecuador          402
## 28 Peru             394
## 29 Luxembourg       392
## 30 Turkey           391
## # ... with 189 more rows
covid_data %>% filter(!is.na(continent), !is.na(hosp_patients)) %>% # group_by(location) %>% 
  count(location) %>% arrange(desc(n)) %>% print(n=30)
## # A tibble: 38 x 2
##    location           n
##    <chr>          <int>
##  1 Italy            748
##  2 Estonia          744
##  3 Sweden           744
##  4 Netherlands      743
##  5 Israel           741
##  6 Portugal         737
##  7 Canada           734
##  8 Czechia          732
##  9 Hungary          732
## 10 Slovakia         731
## 11 Cyprus           728
## 12 Belgium          727
## 13 Ireland          727
## 14 Slovenia         727
## 15 France           725
## 16 Luxembourg       721
## 17 Malaysia         719
## 18 United Kingdom   714
## 19 Australia        712
## 20 Austria          711
## 21 Serbia           711
## 22 Switzerland      711
## 23 Denmark          709
## 24 Latvia           706
## 25 Bulgaria         700
## 26 Singapore        692
## 27 Croatia          691
## 28 Poland           681
## 29 Malta            677
## 30 Norway           630
## # ... with 8 more rows

Hospitalisation and Vaccination rates

Now we can focus on a subset of countries that have more complete vaccination and hospitalisation rates data, so we could compare them.

countries_subset <- c("Italy", "United States", "Israel", "United Kingdom",  "France", "Czechia")
# subset our original data to these countries
hosp_data <- covid_data %>% filter(location %in% countries_subset)
# define a new colour palette for these countries
col_pal <- "ggsci::category10_d3"

Let’s look at hospitalisation rates first.

ggplot(hosp_data, aes(x=date, y = hosp_patients,colour=location)) +
  geom_line(size=0.75) + scale_y_continuous(labels=comma) + 
  scale_color_paletteer_d(col_pal) +
  scale_x_date(name = NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  labs(color="Country", y = "Hospitalised patients") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank())

Can you identify the “waves” in each country?
It’s hard to see the details in the countries with lower number of hospitalised patients, how can we improve the visualisation?

Look at hospitalision rates proportional to the population size!
# hosp per population size
p1 <- ggplot(hosp_data, 
       aes(x=date, y = hosp_patients_per_million,colour=location)) +
  geom_line(size=0.75) + 
  scale_y_continuous(labels=comma) + 
  scale_color_paletteer_d(col_pal) +
  scale_x_date(name = NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  labs(color="Country", y = "Hospitalised patients (per million)") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank())
p1

Now let’s try to compare this to vaccination rates

# total vaccination per population
p2 <- ggplot(hosp_data, 
             aes(x=date, y = people_fully_vaccinated_per_hundred/100 ,colour=location)) +
  geom_line(size=0.75, linetype="dashed") + 
  guides(color = guide_legend(override.aes = list(linetype="solid") ) ) +
  scale_y_continuous(labels=percent) + 
  scale_color_paletteer_d(col_pal) +
  scale_x_date(name = NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  labs(color="Country", y = "Fully vaccinated (percent)") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank())
p2

What will be the best way to compare these values?

p1_narrow <- p1 + guides(color=FALSE) +
  scale_x_date(name = NULL,
               breaks = breaks_width("4 months"), 
               labels = label_date_short())
p2_narrow <- p2 +
  scale_x_date(name = NULL,
               breaks = breaks_width("4 months"), 
               labels = label_date_short())
(p1_narrow + guides(color=FALSE))+ p2_narrow + plot_layout(guides = 'collect')# show graphs side by side

Maybe like this:

(p1 + guides(color=FALSE)) / (p2 + theme(legend.position = "bottom")) #+ plot_layout(guides = 'collect')# show graphs on top of each other

Any suggestions?

There's a lot of empty "real estate" in the vaccination graph, maybe we could trim off 2020?
p3 <- ggplot(hosp_data, 
             aes(x=date, y = people_fully_vaccinated_per_hundred/100 ,colour=location)) +
  geom_line(size=0.75, linetype="dashed") +
  guides(color = guide_legend(override.aes = list(linetype="solid") ) ) +
  scale_y_continuous(labels=percent) + 
  scale_color_paletteer_d(col_pal) +
  scale_x_date(name = NULL,
               limits = c(dmy("01-01-2021"), NA),
               breaks = breaks_width("6 months"), 
               labels = label_date_short()) + 
  labs(color="Country", y = "Fully vaccinated (percent)") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank())
(p1_narrow + guides(color=FALSE)) + p3 + plot_layout(guides = 'collect', widths = c(2, 1)) # maybe like this?

Let’s try to present them on the same graph (note the trick with the secondary y-axis).

# show on the same graph
p4 <- ggplot(hosp_data, 
       aes(x=date, colour=location)) +
  geom_line(aes(y = hosp_patients_per_million), size=0.75) + 
  geom_line(aes(y = people_fully_vaccinated_per_hundred*10), size=0.75, linetype="dashed") + 
  scale_y_continuous(labels=comma, name = "Hospitalised patients per million (solid)",
                     # Add a second axis and specify its features
                     sec.axis = sec_axis(trans=~./10,  name="Fully vaccinated per hundred (dashed)")) + 
  scale_color_paletteer_d(col_pal) +
  scale_x_date(NULL,
               breaks = breaks_width("2 months"), 
               labels = label_date_short()) + 
  labs(color="Country") +
  theme_bw(def_text_size) + 
  theme(panel.grid.minor = element_blank()) 
p4

Probably best to present them on the same graph (note the trick with the secondary y-axis), but for each country separately (done with the facet_wrap() function).

# show on the same graph, but separate each country
p4 + 
  scale_x_date(NULL,
               breaks = breaks_width("4 months"), 
               labels = label_date_short()) +
  facet_wrap(~location)

Save the plot to a folder.

# create output folder
dir.create("./output", showWarnings = FALSE)
# save the plot to pdf file
ggsave("output/hospit_vacc_rates_facet_country.pdf", width=14, height = 8)

Questions

  1. What other variables we could analyse?
  2. Any correlated variables?
  3. What we should take into account that might bias the results or the true status of the pandemic?
1. Mortalities (Case Fatality Rate)?  
2. Suggestions? (cases per population density, vaccination rates by country income, deaths by number of beds per capita, etc.
3. Level of reporting in each country...  

Additional Resources

  • Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository and website
  • EU 14-days COVID-19 data for download in CSV/Excel format link
  • Be awesome in ggplot2: A Practical Guide to be Highly Effective – R software and data visualization (link)
  • The R graph gallery - From the creator of “Data to Viz” - a comprehensive gallery of R charts, with reproducible code examples (link)
  • COVID-19 vaccination data in Our World in Data site
  • My very own COVID-19 dashboard (created in R, needs updating)

Now take the plunge and start practicing with your own data!!

tweet_embed("https://twitter.com/YannisMarkonis/status/1276211134186602499")

Contact

Please contact me at i.bar@griffith.edu.au for any questions or comments.

References

Mathieu E, Ritchie H, Ortiz-Ospina E, et al. (2021) A global database of COVID-19 vaccinations. Nat Hum Behav 5:947–953. doi: 10.1038/s41562-021-01122-8